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Convergence of Science to Machine Learning and IoT

Today while working with my friends on a project related to the dental instruments just a topic came up regarding understanding machines... Machines that implement black-box model or similar machine learning algorithms. At this very moment I had a thought. If every machine were to be equipped with a computer or an embedded system implementing such algorithms, it could easily replace humans in return eliminating human error and creating jobs in such fields (of course, AI should lack consciousness for this).

By this it is clear that machine learning, in other words experiential learning can be the best solution to problems such as human error, wear and tear error, environment influenced errors, etc. But machine learning alone cannot survive as it needs data to learn and develop. Here comes Internet of Things, the data acquisition medium for the ML algorithms. Data collected from the sensor nodes needs to be accumulated or distributed among the similar type of machines with the help of IoT for learning.

Clearly from the above thought Machine Learning and Internet of Things can revolutionise technology, medical science, agricultural science, etc. and in short, Science as a whole. This means almost all fields of science will converge to physical computing or embedded systems, electronics, communication networks, sensor technology, biological science, chemical science and computer science, in short, Machine Learning and IoT.

Again even if the above mention fields get saturated and get replaced by machines, everything is going to converge to MATHEMATICS, as machine learning and IoT protocols are just mathematical models.

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